1. Field of the Invention
In general, the present invention provides a computerized data mining system, method and program product. Specifically, the present invention provides a network-based system for obtaining and executing a data mining model to provide business analytics.
2. Related Art
As businesses increasingly rely upon computer technology to perform essential functions, data mining is rapidly becoming vital to business success. Specifically, many businesses gather various types of data about the business and/or its customers so that operations can be gauged and optimized. Typically, a business will gather data into a database or the like and then utilize a data mining model to analyze the data.
Unfortunately, many companies are unable to flexibly integrate data analytics into business processes because of the complexity, expense, and incomprehensibility often involved. For example, in terms of infrastructure, companies often must invest substantial resources to build data warehouses, implement servers, hire “mining experts” and IT staff to use mining software, etc. In terms of processes, companies must then spend considerable time mapping and tuning between data and mining functions. To this extent, business analysts are typically required to possess the mining domain knowledge to choose the best mining algorithm and select appropriate data. In general, there can be more than twenty technically oriented parameters to tune and map. However, in reality, business analysts might know their data and business objectives well, but might not have an in-depth knowledge of the mining algorithm and/or the tuning parameters.
In fact, very few segments in industry have the resources (human and financial) to deploy sophisticated data analytics solutions such as data mining and scoring. Basically to deploy data mining techniques, companies have two choices: (1) acquire data mining tools and hire an industry specialist to prepare the environment and set up the tool to be used; or (2) hire external consultants to avoid the lack of skills, and large investments in infrastructure companies. Both cases are an extremely expensive proposition for most companies due to the complexity of data integration and the tight binding of complex models to the analytics process.
Heretofore, attempts have been made at automating the data mining process. No existing system, however, allows data mining models to be iteratively generated and/or executed in parallel. That is, any existing systems that provide for the generation or execution of data mining models do so one data mining model at a time.
In view of the foregoing, there exists a need for a computerized data mining system, method and program product. Specifically, a need exists for a system that can iteratively generate customized data mining models in parallel based on permutations of user data, user-provided business parameters and/or a set of model generation algorithms. A further need exists for a system that allows a user to select existing data mining models from a library of existing data mining models that is assembled based on the business parameters. Still yet, another need exists for a system that can execute multiple customized or existing data mining models in parallel.